1,721,065 research outputs found
CRYPTOCURRENCIES in FINANCE: REVIEW and APPLICATIONS
Full text linkThe literature has recently begun to investigate the properties of cryptocurrency markets to identify key drivers for the use of cryptocurrencies in investment strategies. This paper provides a comprehensive review on the financial applications of Bitcoin. The focus is on three lines of research: price formation, detection of market inefficiency, and diversified portfolio construction. Topics such as market micro-structure and the interplay between different cryptocurrencies are only touched on briefly. We observe that many empirical studies find that Bitcoin markets are inefficient, with huge price fluctuations and long-range memory, and that these markets are heavily influenced by news and sector-specific events, or by infrastructure conditions such as volume trading and market liquidity. Nevertheless, astonishing price appreciations and modest correlation values versus other asset classes have contributed significantly to motivate applications of Bitcoin to investment and diversification. Future research may address practical implementations of such solutions and investigate the long-term sustainability and viability of these investment strategies
News and subjective beliefs: A Bayesian approach to Bitcoin investments
No full textThe use of crypto-currencies in financial applications is receiving increasing interest. This paper relies on a Bayesian framework that combines market-neutral information with subjective beliefs to show an application of how Bitcoin can be exploited to build diversified investment strategies. By means of an intuitive procedure based on the Black and Litterman model, I propose to relate portfolio construction with the role of news in generating investors’ subjective beliefs, which are computed according to market reactions occurred after similar announcement events in the recent past. To test this approach, the analysis refers to an extremely volatile market phase for Bitcoin such as the interval from mid-2017 to mid-2018. Results indicate that Bitcoin can contribute to improve the risk-adjusted performances of diversified portfolios and that investors’ subjective beliefs can help to interpret the fundamental drivers of crypto-currencies’ market behaviors. This approach may also stimulate the investigation of more sophisticated strategies built according to the relationships between news and investors’ personal views on Bitcoin market dynamics
With or without U(K): A pre-Brexit network analysis of the EU ETS
Full text linkThe European Emission Trading System (EU ETS) is commonly regarded as the key pillar of the European climate policy and as the main unifying tool to create a unique carbon price all over Europe. The UK has always played a crucial role in the EU ETS, being one of the most active national registry and a crucial hub for the exchange of allowances in the market. Brexit, therefore, could deeply modify the number and directions of such exchanges as well as the centrality of the other countries in this system. To investigate these issues, the present paper exploits network analysis tools to compare the structure of the EU ETS market in its first two phases with and without the UK, investigating a few different scenarios that might emerge from a possible reallocation of the transactions that have involved UK partners. We find that without the UK the EU ETS network would become in general much more homogeneous, though results may change focusing on the type of accounts involved in the transactions
EU ETS facets in the net: Structure and evolution of the EU ETS network
Full text linkIn this work, we investigate which countries have been more central during Phases I and II of the European Emission Trading Scheme (EU ETS) with respect to the different types of accounts operating in the system. We borrow a set of centrality measures from Network Theory's tools to describe how the structure of the system has evolved over time and to identify which countries have been in the core or in the periphery of the network. Performing partitions on the different types of accounts and transactions characterizing the EU ETS, we investigate whether intermediaries have affected the overall structure of the system. From the analysis of the European Union Transaction Log data over the period 2005–2012, we find that some national registries (France, Denmark, Germany, United Kingdom, The Netherlands) were much more central than others in the network. Empirical evidence, moreover, shows that some account holders strategically opened additional accounts in the more central registries, thus reinforcing their centrality in the network. Finally, it turns out that Person Holding Accounts (PHAs) have played a prominent role in the transaction of permits, heavily influencing the configuration of the system. This motivates further research on the impact of non-regulated entities in the EU ETS design
Revealing Pairs-trading opportunities with long short-term memory networks
Full text linkThis work examines a deep learning approach to complement investors’ practices for the identification of pairs-trading opportunities among cointegrated stocks. We refer to the reversal effect, consisting in the fact that temporarily market deviations are likely to correct and finally converge again, to generate valuable pairs-trading signals based on the application of Long Short-Term Memory networks (LSTM). Specifically, we propose to use the LSTM to estimate the probability of a stock to exhibit increasing market returns in the near future compared to its peers, and we compare and combine these predictions with trading practices based on sorting stocks according to either price or returns gaps. In so doing, we investigate the ability of our proposed approach to provide valuable signals under different perspectives including variations in the investment horizons, transaction costs and weighting schemes. Our analysis shows that strategies including such predictions can contribute to improve portfolio performances providing predictive signals whose information content goes above and beyond the one embedded in both price and returns gaps
On the fragility of the Italian economic territories under SARS-COV2 lockdown policies
Full text linkWe leverage a granular representation of mobility patterns before and during the first wave of SARS-COV2 in Italy to investigate the economic consequences of various forms of lockdown policies when accounting for mobility restrictions between and within local jurisdictions, i.e. municipalities, provinces and regions. We provide an analytical characterization of the rate of economic losses using a network-based spectral method. The latter treats the spread of contagion of economic losses due to commuting restrictions as a dynamical system stability problem. Our results indicate that the interplay between lower level of smartworking and the polarization of commuting flows to fewer local labor hubs in the South of Italy makes Southern territories extremely important in spreading economic losses. We estimate an economic contraction of total income derived from commuting restrictions in the range of 10-30% depending on the economic assumptions. However, alternative policies proposed during the second wave of SARS-COV2 can pose a greater risk to Northern areas due to their higher degree of mobility between jurisdictions than Southern ones. The direction of economic losses tend to propagate from large to medium-small jurisdictions across all alternative lockdown policies we tested. Our study shows how complex mobility patterns can have unequal consequences to economic losses across the country and call for more tailored implementation of restrictions to balance the containment of contagion with the need to sustain economic output
Commodity prices co-movements and financial stability: A multidimensional visibility nexus with climate conditions
Full text linkThis paper investigates the nexus between climate-related variables, commodity price co-movements and financial stability. First, we project the commodity price time series onto a multilayer network. Centrality measures computed on the network are used to detect the existence of common trends between the series and to characterize the role of different nodes during phases of market downturns and upturns, unveiling the onset of financial instability. Then, an econometric analysis is introduced to show how climate-related variables affect financial stability by influencing co-movements of commodity prices. Overall, the paper reveals how synthetic indicators of commodity price co-movements generate valuable signals to study the nexus between climate-related conditions and the dynamics of financial systems
A tale of two layers: The mutual relationship between bitcoin and lightning network
Full text linkA major concern of the adoption and scalability of Blockchain technologies refers to their efficient use for payments. In this work, we analyze how Lightning Network (LN), which represents a relevant infrastructural novelty, is influenced by the market dynamics of its referring cryptocurrency, namely Bitcoin. In so doing, we focus on how the LN is efficient in performing transactions and we relate this feature to the market conditions of Bitcoin. By applying the Toda–Yamamoto variant of Granger-causality, we note that market conditions of Bitcoin do not significantly influence the topological configuration of the LN. Hence, although the LN represents a second layer on the Bitcoin blockchain, our findings suggest that its efficient functioning does not appear to be related to the simple market performance of its underlying cryptocurrency and, in particular, of its volatile market fluctuations. This result may therefore contribute to shed light on the practical usage of the LN as a blockchain technology to favor transactions
Market instability and the size-variance relationship
Full text linkWe show that some key features of the behavior of mutual funds is accounted for by a stochastic model of proportional growth. We find that the negative dependence of the variance of funds’ growth rates on size is well described by an approximate power law. We discover that during periods of crisis the volatility of the largest funds’ growth rates increases with respect to mid-sized funds. Our result reveals that a lower and flatter slope provides relevant information on the structure of the system. We find that growth rates volatility poorly depends on the size of the funds, thus questioning the benefits of diversification achieved by larger funds. Our findings show that the slope of the size-variance relationship can be used as a synthetic indicator to monitor the intensity of instabilities and systemic risk in financial markets
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